Reducing Power Spectral Density of Eye Blink Artifact through Improved Genetic Algorithm

摘要

It is a known fact that brain's neurological activity is a source of control in any brain-computer interface (BCI) system and artifacts are undesirable signals. We present a technique to reduce electrooculogram (EOG) artifacts that corrupt electroencephalogram (EEC) signals in BCI applications. The developed genetic algorithm based independent component analysis (GALME-1CA) uses mutual information (MI) as a fitness function to reduce the EOG artifacts, which corrupt the recorded EEG channels. The genetic algorithm using large mutation rates and population elitist selection (GALME) enables local as well as global search to be performed in a balanced way. We tested the algorithm with simulated data and EEG signals corrupted with EOG artifacts from BCI competition IV dataset.